Overview

Dataset statistics

Number of variables17
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory140.1 B

Variable types

Numeric14
Categorical3

Alerts

Total has constant value "0"Constant
Max is highly overall correlated with Avg and 3 other fieldsHigh correlation
Avg is highly overall correlated with Max and 7 other fieldsHigh correlation
Min is highly overall correlated with Avg and 4 other fieldsHigh correlation
Max.1 is highly overall correlated with Avg and 6 other fieldsHigh correlation
Avg.1 is highly overall correlated with Avg and 6 other fieldsHigh correlation
Min.1 is highly overall correlated with Avg and 6 other fieldsHigh correlation
Avg.2 is highly overall correlated with Min.1 and 1 other fieldsHigh correlation
Min.2 is highly overall correlated with Max and 4 other fieldsHigh correlation
Max.3 is highly overall correlated with Avg.3High correlation
Avg.3 is highly overall correlated with Max.3 and 1 other fieldsHigh correlation
Max.4 is highly overall correlated with Max and 6 other fieldsHigh correlation
Avg.4 is highly overall correlated with Avg and 4 other fieldsHigh correlation
Min.4 is highly overall correlated with Max and 4 other fieldsHigh correlation
Max.2 is highly overall correlated with Min.3High correlation
Min.3 is highly overall correlated with Avg.3 and 1 other fieldsHigh correlation
Jan is uniformly distributedUniform
Jan has unique valuesUnique

Reproduction

Analysis started2023-02-04 20:31:19.056228
Analysis finished2023-02-04 20:32:07.952408
Duration48.9 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Jan
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:08.120135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q18.5
median16
Q323.5
95-th percentile29.5
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)0.56825757
Kurtosis-1.2
Mean16
Median Absolute Deviation (MAD)8
Skewness0
Sum496
Variance82.666667
MonotonicityStrictly increasing
2023-02-04T22:32:08.384212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1
 
3.2%
17 1
 
3.2%
30 1
 
3.2%
29 1
 
3.2%
28 1
 
3.2%
27 1
 
3.2%
26 1
 
3.2%
25 1
 
3.2%
24 1
 
3.2%
23 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1 1
3.2%
2 1
3.2%
3 1
3.2%
4 1
3.2%
5 1
3.2%
6 1
3.2%
7 1
3.2%
8 1
3.2%
9 1
3.2%
10 1
3.2%
ValueCountFrequency (%)
31 1
3.2%
30 1
3.2%
29 1
3.2%
28 1
3.2%
27 1
3.2%
26 1
3.2%
25 1
3.2%
24 1
3.2%
23 1
3.2%
22 1
3.2%

Max
Real number (ℝ)

Distinct14
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41
Minimum30
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:08.674922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile31
Q136
median39
Q345.5
95-th percentile54
Maximum55
Range25
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation6.8896057
Coefficient of variation (CV)0.16803916
Kurtosis-0.41996302
Mean41
Median Absolute Deviation (MAD)4
Skewness0.51646118
Sum1271
Variance47.466667
MonotonicityNot monotonic
2023-02-04T22:32:08.912866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
39 4
12.9%
36 4
12.9%
41 4
12.9%
37 3
9.7%
48 2
6.5%
34 2
6.5%
45 2
6.5%
30 2
6.5%
46 2
6.5%
54 2
6.5%
Other values (4) 4
12.9%
ValueCountFrequency (%)
30 2
6.5%
32 1
 
3.2%
34 2
6.5%
36 4
12.9%
37 3
9.7%
39 4
12.9%
41 4
12.9%
43 1
 
3.2%
45 2
6.5%
46 2
6.5%
ValueCountFrequency (%)
55 1
 
3.2%
54 2
6.5%
52 1
 
3.2%
48 2
6.5%
46 2
6.5%
45 2
6.5%
43 1
 
3.2%
41 4
12.9%
39 4
12.9%
37 3
9.7%

Avg
Real number (ℝ)

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.883871
Minimum24.5
Maximum42.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:09.153905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum24.5
5-th percentile24.85
Q128.25
median31.9
Q335.45
95-th percentile40.2
Maximum42.1
Range17.6
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation4.768934
Coefficient of variation (CV)0.14957199
Kurtosis-0.59255918
Mean31.883871
Median Absolute Deviation (MAD)4
Skewness0.30229956
Sum988.4
Variance22.742731
MonotonicityNot monotonic
2023-02-04T22:32:09.410334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
28.6 2
 
6.5%
27.8 2
 
6.5%
31.9 2
 
6.5%
32.6 1
 
3.2%
36.6 1
 
3.2%
40.5 1
 
3.2%
42.1 1
 
3.2%
39.9 1
 
3.2%
36.9 1
 
3.2%
24.6 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
24.5 1
3.2%
24.6 1
3.2%
25.1 1
3.2%
25.3 1
3.2%
26.6 1
3.2%
27.8 2
6.5%
27.9 1
3.2%
28.6 2
6.5%
28.7 1
3.2%
30 1
3.2%
ValueCountFrequency (%)
42.1 1
3.2%
40.5 1
3.2%
39.9 1
3.2%
36.9 1
3.2%
36.8 1
3.2%
36.6 1
3.2%
36.1 1
3.2%
36 1
3.2%
34.9 1
3.2%
34.2 1
3.2%

Min
Real number (ℝ)

Distinct11
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.129032
Minimum14
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:09.662045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14
Q118
median25
Q329
95-th percentile34
Maximum34
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5612089
Coefficient of variation (CV)0.27192176
Kurtosis-1.158563
Mean24.129032
Median Absolute Deviation (MAD)5
Skewness-0.092846642
Sum748
Variance43.049462
MonotonicityNot monotonic
2023-02-04T22:32:09.892642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
16 4
12.9%
23 4
12.9%
34 4
12.9%
25 4
12.9%
30 3
9.7%
27 3
9.7%
14 3
9.7%
28 2
6.5%
18 2
6.5%
32 1
 
3.2%
ValueCountFrequency (%)
14 3
9.7%
16 4
12.9%
18 2
6.5%
19 1
 
3.2%
23 4
12.9%
25 4
12.9%
27 3
9.7%
28 2
6.5%
30 3
9.7%
32 1
 
3.2%
ValueCountFrequency (%)
34 4
12.9%
32 1
 
3.2%
30 3
9.7%
28 2
6.5%
27 3
9.7%
25 4
12.9%
23 4
12.9%
19 1
 
3.2%
18 2
6.5%
16 4
12.9%

Max.1
Real number (ℝ)

Distinct10
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.16129
Minimum21
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:10.131329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q125
median28
Q333
95-th percentile36
Maximum41
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.1774947
Coefficient of variation (CV)0.17754683
Kurtosis-0.80798035
Mean29.16129
Median Absolute Deviation (MAD)4
Skewness0.30647472
Sum904
Variance26.806452
MonotonicityNot monotonic
2023-02-04T22:32:10.370385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
23 6
19.4%
32 5
16.1%
28 4
12.9%
36 4
12.9%
25 3
9.7%
27 3
9.7%
34 3
9.7%
21 1
 
3.2%
30 1
 
3.2%
41 1
 
3.2%
ValueCountFrequency (%)
21 1
 
3.2%
23 6
19.4%
25 3
9.7%
27 3
9.7%
28 4
12.9%
30 1
 
3.2%
32 5
16.1%
34 3
9.7%
36 4
12.9%
41 1
 
3.2%
ValueCountFrequency (%)
41 1
 
3.2%
36 4
12.9%
34 3
9.7%
32 5
16.1%
30 1
 
3.2%
28 4
12.9%
27 3
9.7%
25 3
9.7%
23 6
19.4%
21 1
 
3.2%

Avg.1
Real number (ℝ)

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.116129
Minimum15.9
Maximum35.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:10.622663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum15.9
5-th percentile17.7
Q120.95
median23.8
Q328.45
95-th percentile34
Maximum35.8
Range19.9
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation5.5616003
Coefficient of variation (CV)0.22143541
Kurtosis-0.89189279
Mean25.116129
Median Absolute Deviation (MAD)4.2
Skewness0.3096877
Sum778.6
Variance30.931398
MonotonicityNot monotonic
2023-02-04T22:32:10.886087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
34 2
 
6.5%
23.3 1
 
3.2%
22.5 1
 
3.2%
28.6 1
 
3.2%
35.8 1
 
3.2%
29.1 1
 
3.2%
22.8 1
 
3.2%
17.5 1
 
3.2%
20 1
 
3.2%
18.1 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
15.9 1
3.2%
17.5 1
3.2%
17.9 1
3.2%
18.1 1
3.2%
19 1
3.2%
19.6 1
3.2%
20 1
3.2%
20.4 1
3.2%
21.5 1
3.2%
21.6 1
3.2%
ValueCountFrequency (%)
35.8 1
3.2%
34 2
6.5%
33.8 1
3.2%
33.1 1
3.2%
31.4 1
3.2%
29.1 1
3.2%
28.6 1
3.2%
28.3 1
3.2%
28 1
3.2%
27.7 1
3.2%

Min.1
Real number (ℝ)

Distinct13
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.903226
Minimum10
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:11.121828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q114
median19
Q325
95-th percentile30
Maximum32
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.6751022
Coefficient of variation (CV)0.33537791
Kurtosis-1.1098543
Mean19.903226
Median Absolute Deviation (MAD)5
Skewness0.043980393
Sum617
Variance44.556989
MonotonicityNot monotonic
2023-02-04T22:32:11.377752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
23 4
12.9%
10 4
12.9%
14 3
9.7%
19 3
9.7%
16 2
 
6.5%
18 2
 
6.5%
21 2
 
6.5%
30 2
 
6.5%
25 2
 
6.5%
27 2
 
6.5%
Other values (3) 5
16.1%
ValueCountFrequency (%)
10 4
12.9%
12 2
6.5%
14 3
9.7%
16 2
6.5%
18 2
6.5%
19 3
9.7%
21 2
6.5%
23 4
12.9%
25 2
6.5%
27 2
6.5%
ValueCountFrequency (%)
32 1
 
3.2%
30 2
6.5%
28 2
6.5%
27 2
6.5%
25 2
6.5%
23 4
12.9%
21 2
6.5%
19 3
9.7%
18 2
6.5%
16 2
6.5%

Max.2
Categorical

Distinct4
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size376.0 B
93
18 
100
86
87
 
1

Length

Max length3
Median length2
Mean length2.2580645
Min length2

Characters and Unicode

Total characters70
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row86
2nd row93
3rd row93
4th row100
5th row87

Common Values

ValueCountFrequency (%)
93 18
58.1%
100 8
25.8%
86 4
 
12.9%
87 1
 
3.2%

Length

2023-02-04T22:32:11.614003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-04T22:32:11.857992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
93 18
58.1%
100 8
25.8%
86 4
 
12.9%
87 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
9 18
25.7%
3 18
25.7%
0 16
22.9%
1 8
11.4%
8 5
 
7.1%
6 4
 
5.7%
7 1
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 18
25.7%
3 18
25.7%
0 16
22.9%
1 8
11.4%
8 5
 
7.1%
6 4
 
5.7%
7 1
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 70
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 18
25.7%
3 18
25.7%
0 16
22.9%
1 8
11.4%
8 5
 
7.1%
6 4
 
5.7%
7 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 18
25.7%
3 18
25.7%
0 16
22.9%
1 8
11.4%
8 5
 
7.1%
6 4
 
5.7%
7 1
 
1.4%

Avg.2
Real number (ℝ)

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.019355
Minimum64.1
Maximum98.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:12.107106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum64.1
5-th percentile66.35
Q172.35
median75.9
Q381.45
95-th percentile97.65
Maximum98.3
Range34.2
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation9.3898321
Coefficient of variation (CV)0.1203526
Kurtosis0.038744698
Mean78.019355
Median Absolute Deviation (MAD)4.3
Skewness0.89457028
Sum2418.6
Variance88.168946
MonotonicityNot monotonic
2023-02-04T22:32:12.373163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
72.8 2
 
6.5%
77.3 2
 
6.5%
69.1 1
 
3.2%
97.5 1
 
3.2%
73.4 1
 
3.2%
77.7 1
 
3.2%
74 1
 
3.2%
86 1
 
3.2%
76 1
 
3.2%
64.1 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
64.1 1
3.2%
64.9 1
3.2%
67.8 1
3.2%
69 1
3.2%
69.1 1
3.2%
71.3 1
3.2%
71.6 1
3.2%
72 1
3.2%
72.7 1
3.2%
72.8 2
6.5%
ValueCountFrequency (%)
98.3 1
3.2%
97.8 1
3.2%
97.5 1
3.2%
91.1 1
3.2%
91 1
3.2%
88.9 1
3.2%
86 1
3.2%
82.3 1
3.2%
80.6 1
3.2%
79 1
3.2%

Min.2
Real number (ℝ)

Distinct21
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.096774
Minimum27
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:12.627119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile31
Q144
median53
Q363
95-th percentile93
Maximum93
Range66
Interquartile range (IQR)19

Descriptive statistics

Standard deviation18.776501
Coefficient of variation (CV)0.33471623
Kurtosis-0.39102063
Mean56.096774
Median Absolute Deviation (MAD)9
Skewness0.62181949
Sum1739
Variance352.55699
MonotonicityNot monotonic
2023-02-04T22:32:12.873682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
93 3
 
9.7%
81 3
 
9.7%
53 3
 
9.7%
56 3
 
9.7%
44 2
 
6.5%
60 2
 
6.5%
52 1
 
3.2%
38 1
 
3.2%
49 1
 
3.2%
37 1
 
3.2%
Other values (11) 11
35.5%
ValueCountFrequency (%)
27 1
3.2%
29 1
3.2%
33 1
3.2%
35 1
3.2%
37 1
3.2%
38 1
3.2%
41 1
3.2%
44 2
6.5%
45 1
3.2%
47 1
3.2%
ValueCountFrequency (%)
93 3
9.7%
81 3
9.7%
75 1
 
3.2%
65 1
 
3.2%
61 1
 
3.2%
60 2
6.5%
56 3
9.7%
53 3
9.7%
52 1
 
3.2%
49 1
 
3.2%

Max.3
Real number (ℝ)

Distinct16
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.193548
Minimum3
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:13.121911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q17.5
median13
Q315
95-th percentile21.5
Maximum22
Range19
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation5.5040552
Coefficient of variation (CV)0.45139077
Kurtosis-0.87245663
Mean12.193548
Median Absolute Deviation (MAD)5
Skewness0.28978359
Sum378
Variance30.294624
MonotonicityNot monotonic
2023-02-04T22:32:13.374577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
13 6
19.4%
6 3
9.7%
15 2
 
6.5%
14 2
 
6.5%
5 2
 
6.5%
8 2
 
6.5%
22 2
 
6.5%
9 2
 
6.5%
7 2
 
6.5%
21 2
 
6.5%
Other values (6) 6
19.4%
ValueCountFrequency (%)
3 1
 
3.2%
5 2
 
6.5%
6 3
9.7%
7 2
 
6.5%
8 2
 
6.5%
9 2
 
6.5%
10 1
 
3.2%
12 1
 
3.2%
13 6
19.4%
14 2
 
6.5%
ValueCountFrequency (%)
22 2
 
6.5%
21 2
 
6.5%
20 1
 
3.2%
18 1
 
3.2%
17 1
 
3.2%
15 2
 
6.5%
14 2
 
6.5%
13 6
19.4%
12 1
 
3.2%
10 1
 
3.2%

Avg.3
Real number (ℝ)

Distinct23
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0032258
Minimum1.6
Maximum14.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:13.634106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile1.75
Q12.95
median5.8
Q37.1
95-th percentile13.1
Maximum14.8
Range13.2
Interquartile range (IQR)4.15

Descriptive statistics

Standard deviation3.6706479
Coefficient of variation (CV)0.61144591
Kurtosis0.20628244
Mean6.0032258
Median Absolute Deviation (MAD)2.3
Skewness0.95176991
Sum186.1
Variance13.473656
MonotonicityNot monotonic
2023-02-04T22:32:13.944701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
7.2 3
 
9.7%
3 2
 
6.5%
2.3 2
 
6.5%
2.9 2
 
6.5%
7 2
 
6.5%
1.6 2
 
6.5%
5.3 2
 
6.5%
14.8 1
 
3.2%
11.6 1
 
3.2%
6.9 1
 
3.2%
Other values (13) 13
41.9%
ValueCountFrequency (%)
1.6 2
6.5%
1.9 1
3.2%
2.3 2
6.5%
2.6 1
3.2%
2.9 2
6.5%
3 2
6.5%
3.5 1
3.2%
3.7 1
3.2%
5.1 1
3.2%
5.3 2
6.5%
ValueCountFrequency (%)
14.8 1
 
3.2%
13.4 1
 
3.2%
12.8 1
 
3.2%
12.4 1
 
3.2%
11.6 1
 
3.2%
7.2 3
9.7%
7 2
6.5%
6.9 1
 
3.2%
6.8 1
 
3.2%
6.6 1
 
3.2%

Min.3
Categorical

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size376.0 B
0
22 
2
7
 
1
9
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)9.7%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row7

Common Values

ValueCountFrequency (%)
0 22
71.0%
2 6
 
19.4%
7 1
 
3.2%
9 1
 
3.2%
1 1
 
3.2%

Length

2023-02-04T22:32:14.194216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-04T22:32:14.492616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 22
71.0%
2 6
 
19.4%
7 1
 
3.2%
9 1
 
3.2%
1 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 22
71.0%
2 6
 
19.4%
7 1
 
3.2%
9 1
 
3.2%
1 1
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22
71.0%
2 6
 
19.4%
7 1
 
3.2%
9 1
 
3.2%
1 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22
71.0%
2 6
 
19.4%
7 1
 
3.2%
9 1
 
3.2%
1 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22
71.0%
2 6
 
19.4%
7 1
 
3.2%
9 1
 
3.2%
1 1
 
3.2%

Max.4
Real number (ℝ)

Distinct8
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.496774
Minimum28.1
Maximum28.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:14.746383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum28.1
5-th percentile28.25
Q128.4
median28.5
Q328.6
95-th percentile28.8
Maximum28.9
Range0.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1741338
Coefficient of variation (CV)0.0061106496
Kurtosis0.86362017
Mean28.496774
Median Absolute Deviation (MAD)0.1
Skewness0.17413516
Sum883.4
Variance0.030322581
MonotonicityNot monotonic
2023-02-04T22:32:15.146802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
28.6 9
29.0%
28.5 8
25.8%
28.4 5
16.1%
28.3 4
12.9%
28.9 2
 
6.5%
28.7 1
 
3.2%
28.2 1
 
3.2%
28.1 1
 
3.2%
ValueCountFrequency (%)
28.1 1
 
3.2%
28.2 1
 
3.2%
28.3 4
12.9%
28.4 5
16.1%
28.5 8
25.8%
28.6 9
29.0%
28.7 1
 
3.2%
28.9 2
 
6.5%
ValueCountFrequency (%)
28.9 2
 
6.5%
28.7 1
 
3.2%
28.6 9
29.0%
28.5 8
25.8%
28.4 5
16.1%
28.3 4
12.9%
28.2 1
 
3.2%
28.1 1
 
3.2%

Avg.4
Real number (ℝ)

Distinct8
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.422581
Minimum28
Maximum28.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:15.434360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile28.15
Q128.4
median28.4
Q328.5
95-th percentile28.7
Maximum28.8
Range0.8
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.17646468
Coefficient of variation (CV)0.0062086088
Kurtosis0.73333565
Mean28.422581
Median Absolute Deviation (MAD)0.1
Skewness-0.17275059
Sum881.1
Variance0.031139785
MonotonicityNot monotonic
2023-02-04T22:32:15.679187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
28.4 10
32.3%
28.5 9
29.0%
28.2 4
 
12.9%
28.6 3
 
9.7%
28.8 2
 
6.5%
28.3 1
 
3.2%
28.1 1
 
3.2%
28 1
 
3.2%
ValueCountFrequency (%)
28 1
 
3.2%
28.1 1
 
3.2%
28.2 4
 
12.9%
28.3 1
 
3.2%
28.4 10
32.3%
28.5 9
29.0%
28.6 3
 
9.7%
28.8 2
 
6.5%
ValueCountFrequency (%)
28.8 2
 
6.5%
28.6 3
 
9.7%
28.5 9
29.0%
28.4 10
32.3%
28.3 1
 
3.2%
28.2 4
 
12.9%
28.1 1
 
3.2%
28 1
 
3.2%

Min.4
Real number (ℝ)

Distinct9
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.351613
Minimum27.9
Maximum28.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-02-04T22:32:15.930321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum27.9
5-th percentile28.05
Q128.3
median28.4
Q328.45
95-th percentile28.55
Maximum28.7
Range0.8
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.17864484
Coefficient of variation (CV)0.0063010468
Kurtosis0.4725202
Mean28.351613
Median Absolute Deviation (MAD)0.1
Skewness-0.77815765
Sum878.9
Variance0.031913978
MonotonicityNot monotonic
2023-02-04T22:32:16.289846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
28.4 13
41.9%
28.5 6
19.4%
28.1 4
 
12.9%
28.3 3
 
9.7%
28.2 1
 
3.2%
28.6 1
 
3.2%
28.7 1
 
3.2%
28 1
 
3.2%
27.9 1
 
3.2%
ValueCountFrequency (%)
27.9 1
 
3.2%
28 1
 
3.2%
28.1 4
 
12.9%
28.2 1
 
3.2%
28.3 3
 
9.7%
28.4 13
41.9%
28.5 6
19.4%
28.6 1
 
3.2%
28.7 1
 
3.2%
ValueCountFrequency (%)
28.7 1
 
3.2%
28.6 1
 
3.2%
28.5 6
19.4%
28.4 13
41.9%
28.3 3
 
9.7%
28.2 1
 
3.2%
28.1 4
 
12.9%
28 1
 
3.2%
27.9 1
 
3.2%

Total
Categorical

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size376.0 B
0
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
100.0%

Length

2023-02-04T22:32:16.562979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-04T22:32:16.859999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 31
100.0%

Most occurring characters

ValueCountFrequency (%)
0 31
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31
100.0%

Interactions

2023-02-04T22:32:04.010731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:19.506158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:22.875446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:26.195192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:29.715506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:33.230195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:36.701790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:40.214176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:43.487344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:46.892354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:50.227232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:53.731112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:56.999796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:00.452118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:04.236394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:19.877753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:23.107344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:26.434345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:29.973218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:33.458168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:36.932064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:40.442244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:43.717751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:47.121307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:50.458228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:53.948962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:57.234505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:00.691663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:04.466980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:20.090995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:23.341496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:26.705375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:30.209738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:33.684002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:37.192164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:40.660173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:43.946524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:47.401809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:50.687689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:54.201618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:57.475186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:00.933922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:04.691034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:20.321687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:23.565654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:26.936187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:30.479229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:33.913944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:37.413627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:40.898563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:44.188611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:47.637110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:50.934265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:54.430650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:57.704411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:01.312160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:04.931804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:20.566609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:23.789852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:27.177252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:30.799120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:34.171440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:37.660407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:41.130684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:44.417812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:47.863461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:51.168047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:54.659909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:57.995410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:01.547559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:05.186375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:20.792976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:24.050954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:27.406646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:31.064255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:34.397666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:37.893335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:41.353634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:44.643141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:48.091776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:51.395974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:54.893155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:58.255224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:01.799837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:05.425058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:21.026073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:24.283644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:27.666227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:31.331908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:34.626900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:38.128515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:41.586318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:44.882922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:48.327849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:51.636067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:55.122135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:58.498838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:02.035114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:05.674201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:21.266785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:24.509038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:27.887402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:31.568478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:34.848926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:38.435986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:41.835472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:45.273736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:48.555790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:51.883655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:55.363668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:58.749742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:02.267404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:05.905820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:21.488876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:24.737290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:28.116504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:31.809628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:35.073091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:38.769981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:42.107830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:45.504707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:48.797843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:52.118077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:55.593610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:58.979120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:02.500654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:06.134125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:21.714376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:24.962876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:28.520032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:32.050957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:35.306512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:39.000603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:42.329283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:45.727394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:49.022837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:52.355467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:55.834101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:59.210037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:02.762622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:06.386489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:21.951827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:25.193108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:28.789539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:32.283406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:35.549733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:39.228572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:42.555713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:45.972381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:49.255154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:52.619149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:56.066562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:59.452218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:03.003798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:06.608150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:22.188696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:25.414080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:29.020604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:32.515735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:35.791340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:39.461732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:42.777511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:46.200345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:49.485115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:52.872234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:56.288145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:59.685726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:03.274750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:06.850145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:22.417252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:25.673764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:29.254128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:32.746320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:36.033741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:39.705954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:43.027330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:46.426726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:49.730587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:53.256913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:56.537603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:59.934663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:03.518779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:07.083373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:22.641675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:25.947132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:29.483016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:32.994125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:36.269897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:39.963451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:43.256660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:46.666644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:49.982746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:53.490629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:31:56.778194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:00.203152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-04T22:32:03.754616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-04T22:32:17.134712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
JanMaxAvgMinMax.1Avg.1Min.1Avg.2Min.2Max.3Avg.3Max.4Avg.4Min.4Max.2Min.3
Jan1.0000.3670.4350.2010.3350.3210.232-0.081-0.0630.068-0.024-0.220-0.235-0.2500.3860.147
Max0.3671.0000.518-0.1150.3970.166-0.068-0.299-0.5820.034-0.084-0.537-0.477-0.6290.2440.000
Avg0.4350.5181.0000.7220.8210.8150.685-0.0470.0840.3130.231-0.739-0.679-0.7560.0810.381
Min0.201-0.1150.7221.0000.6770.8400.8900.2760.5750.2510.241-0.473-0.446-0.4390.0000.149
Max.10.3350.3970.8210.6771.0000.9220.8000.4020.3920.148-0.024-0.758-0.653-0.6120.0000.385
Avg.10.3210.1660.8150.8400.9221.0000.9380.4900.5750.079-0.035-0.632-0.536-0.4820.2690.059
Min.10.232-0.0680.6850.8900.8000.9381.0000.5270.6880.036-0.047-0.524-0.420-0.3560.4510.285
Avg.2-0.081-0.299-0.0470.2760.4020.4900.5271.0000.828-0.268-0.380-0.0380.0980.2660.3720.466
Min.2-0.063-0.5820.0840.5750.3920.5750.6880.8281.000-0.081-0.132-0.0310.0290.2060.2880.494
Max.30.0680.0340.3130.2510.1480.0790.036-0.268-0.0811.0000.932-0.292-0.286-0.3200.3760.150
Avg.3-0.024-0.0840.2310.241-0.024-0.035-0.047-0.380-0.1320.9321.000-0.186-0.227-0.2570.0000.557
Max.4-0.220-0.537-0.739-0.473-0.758-0.632-0.524-0.038-0.031-0.292-0.1861.0000.9120.8760.0000.000
Avg.4-0.235-0.477-0.679-0.446-0.653-0.536-0.4200.0980.029-0.286-0.2270.9121.0000.8890.0640.000
Min.4-0.250-0.629-0.756-0.439-0.612-0.482-0.3560.2660.206-0.320-0.2570.8760.8891.0000.1140.000
Max.20.3860.2440.0810.0000.0000.2690.4510.3720.2880.3760.0000.0000.0640.1141.0000.600
Min.30.1470.0000.3810.1490.3850.0590.2850.4660.4940.1500.5570.0000.0000.0000.6001.000

Missing values

2023-02-04T22:32:07.398191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-04T22:32:07.852063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

JanMaxAvgMinMax.1Avg.1Min.1Max.2Avg.2Min.2Max.3Avg.3Min.3Max.4Avg.4Min.4Total
013732.6282523.3168669.1442013.4228.628.528.40
123727.9182522.5169380.656136.6028.628.628.50
234326.6182721.6149382.35362.3028.528.428.40
344831.2163221.51410072.027156.5028.428.328.20
453934.9303227.7238775.3601812.8728.328.228.10
563430.0272722.2199372.756147.2228.428.428.30
673627.8232319.6188673.152137.2228.528.428.40
783724.5142115.9109372.838105.1028.628.528.50
893925.1142317.9109375.94962.9028.628.528.40
9104833.1232823.6219371.63751.9028.428.428.30
JanMaxAvgMinMax.1Avg.1Min.1Max.2Avg.2Min.2Max.3Avg.3Min.3Max.4Avg.4Min.4Total
21224628.6162318.1109369.02993.5028.728.628.40
22234131.6192520.0149364.1332212.4028.628.528.40
23244124.6142317.5109377.34572.6028.628.628.50
24255531.9163222.8129372.84183.0028.528.428.40
25265436.9253429.1239376.04772.9028.428.428.30
26274539.9344135.8289386.075135.3128.328.228.10
27285442.1343634.0289374.044137.0228.228.128.00
28294640.5343634.0309377.761216.9028.128.027.90
29304136.6283628.6239373.4532111.6228.328.228.10
30315236.8232824.7199364.935135.8028.328.228.10